From the Romanticists Hamann and Herder, we might claim that that data scientists at BBD platforms create “arbitrary divisions of reality” (i.e., selectively log behaviors in apps and devices) in order to build “castles in the air” (predictive models of one's “true” preferences) [1]}. Put into today's language, pre-defined categories of recorded behavior, which are in turn used by RS to make predictions, are categories of the data scientist's choosing and do not exhaust the reality of the data subject as he interacts with a device or app. Even in so-called “context-aware” recommender systems [2]}, interpretations of the various contexts are one-sided and fail to account for contested interpretations by the data subject. This problem will become worse if BBD platforms continue moving towards reinforcement learning-based RS.
